Method and system for recommender model selection

ABSTRACT

The disclosure herein generally relates to recommender model selection, and, more particularly, to a method and system for selecting a recommender model matching user requirements. The system collects a user requirement including at least one error measure and corresponding Error Value (EV) and Error Tolerance (ET) as input. A recommendation learned model processes the user requirement and selects at least one of a plurality of recommender models as a recommender model matching the user requirement and generates a recommendation.

PRIORITY CLAIM

This U.S. patent application claims priority under 35 U.S.C. § 119 to:India Application No. 201921048644, filed on Nov. 27, 2019. The entirecontents of the aforementioned application are incorporated herein byreference.

TECHNICAL FIELD

This disclosure relates generally to recommender model selection, andmore particularly to a method and system for selecting a recommendermodel matching user requirements.

BACKGROUND

Recommender models are used for processing various types of data forgenerating recommendations. For example, targeted advertisements aregenerated with the intention of attracting customers towards purchasinggoods. The targeted advertisements are generated by processing data suchas purchase history of each customer, product/service specifications,offers, and so on. By processing such data, the recommender modelsextracts information pertaining to purchase interests of the user.

The inventors here have recognized several technical problems with suchconventional systems, as explained below. Each of the recommender modelsmay be having different data processing capabilities and in terms oftype of data being processed by each of such recommender models. Eachuser may be having specific requirements while opting for recommendermodels, and selection of a recommender model that best matches the userrequirements helps in obtaining optimum results.

SUMMARY

Embodiments of the present disclosure present technological improvementsas solutions to one or more of the above-mentioned technical problemsrecognized by the inventors in conventional systems. For example, in oneembodiment, a a processor implemented method for recommender modelselection is provided. In this method, a user requirement is collectedas input, via one or more hardware processors, wherein the userrequirement comprises at least one error measure and corresponding atleast one error value (EV) and error tolerance (ET). Further, thecollected user requirement are processed using a recommendation learnedmodel pre-trained on information pertaining to capability of a pluralityof recommendation models and user requirements, via the one or morehardware processors. The recommendation learned model determinesdynamically, value of EV and ET of the at least one error measure foreach of the plurality of recommender models. The recommendation learnedmodel further determines correlation between the determined value of EVand ET of the at least one error measure for each of the plurality ofrecommender models with the EV and ET in the user requirement, via theone or more hardware processors. Further, at least one of the pluralityof recommender models is determined as a recommender model matching theuser requirement, based on the determined correlation, via the one ormore hardware processors. Further, a recommendation is generated basedon the at least one recommender model determined as matching the userrequirement, via the one or more hardware processors.

In another embodiment, a system for recommender model selection isprovided. The system includes one or more hardware processors, one ormore communication interfaces, and one or more memory storing aplurality of instructions. The plurality of instructions when executedcause the one or more hardware processors to collect a user requirementas input, via one or more hardware processors, wherein the userrequirement comprises at least one error measure and corresponding atleast one error value (EV) and error tolerance (ET). The system furtherprocesses the collected user requirement using a recommendation learnedmodel pre-trained on information pertaining to capability of a pluralityof recommendation models and user requirements, via the one or morehardware processors. The recommendation learned model determinesdynamically, value of EV and ET of the at least one error measure foreach of the plurality of recommender models. The recommendation learnedmodel further determines correlation between the determined value of EVand ET of the at least one error measure for each of the plurality ofrecommender models with the EV and ET in the user requirement, via theone or more hardware processors. Further, at least one of the pluralityof recommender models is determined as a recommender model matching theuser requirement, based on the determined correlation, via the one ormore hardware processors. Further, a recommendation is generated basedon the at least one recommender model determined as matching the userrequirement, via the one or more hardware processors.

In yet another embodiment, a non-transitory computer readable medium forrecommender model selection is provided. The non-transitory computerreadable medium executes the following method for generating arecommender model recommendation. In this method, a user requirement iscollected as input, via one or more hardware processors, wherein theuser requirement comprises at least one error measure and correspondingat least one error value (EV) and error tolerance (ET). Further, thecollected user requirement are processed using a recommendation learnedmodel pre-trained on information pertaining to capability of a pluralityof recommendation models and user requirements, via the one or morehardware processors. The recommendation learned model determinesdynamically, value of EV and ET of the at least one error measure foreach of the plurality of recommender models. The recommendation learnedmodel further determines correlation between the determined value of EVand ET of the at least one error measure for each of the plurality ofrecommender models with the EV and ET in the user requirement, via theone or more hardware processors. Further, at least one of the pluralityof recommender models is determined as a recommender model matching theuser requirement, based on the determined correlation, via the one ormore hardware processors. Further, a recommendation is generated basedon the at least one recommender model determined as matching the userrequirement, via the one or more hardware processors.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate exemplary embodiments and, togetherwith the description, serve to explain the disclosed principles.

FIG. 1 illustrates an exemplary system for recommender model selection,according to some embodiments of the present disclosure.

FIG. 2 is a flow diagram depicting steps involved in the process ofgenerating a recommender model recommendation, using the system of FIG.1, according to some embodiments of the present disclosure.

FIG. 3 is a flow diagram depicting steps involved in the process ofselecting a recommender model from a plurality of recommender models,using the system of FIG. 1, in accordance with some embodiments of thepresent disclosure.

DETAILED DESCRIPTION

Exemplary embodiments are described with reference to the accompanyingdrawings. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears.Wherever convenient, the same reference numbers are used throughout thedrawings to refer to the same or like parts. While examples and featuresof disclosed principles are described herein, modifications,adaptations, and other implementations are possible without departingfrom the scope of the disclosed embodiments. It is intended that thefollowing detailed description be considered as exemplary only, with thetrue scope being indicated by the following claims.

FIG. 1 illustrates an exemplary system for recommender model selection,according to some embodiments of the present disclosure. The system 100includes one or more hardware processors 102, communication interface(s)or input/output (I/O) interface(s) 103, and one or more data storagedevices or memory 101 operatively coupled to the one or more hardwareprocessors 102. The one or more hardware processors 102 can beimplemented as one or more microprocessors, microcomputers,microcontrollers, digital signal processors, central processing units,state machines, graphics controllers, logic circuitries, and/or anydevices that manipulate signals based on operational instructions. Amongother capabilities, the processor(s) 102 are configured to fetch andexecute computer-readable instructions stored in the memory 101, theinstructions when executed cause the one or more hardware processors toperform one or more actions associated with the recommender modelselection being handled by the system 100. In an embodiment, the system100 can be implemented in a variety of computing systems, such as laptopcomputers, notebooks, hand-held devices, workstations, mainframecomputers, servers, a network cloud and the like.

The communication interface(s) 103 can include a variety of software andhardware interfaces, for example, a web interface, a graphical userinterface, and the like and can facilitate multiple communicationswithin a wide variety of networks N/W and protocol types, includingwired networks, for example, LAN, cable, etc., and wireless networks,such as WLAN, cellular, or satellite. In an embodiment, thecommunication interface(s) 103 can include one or more ports forconnecting a number of devices to one another or to another server.

The memory 101 may include any computer-readable medium known in the artincluding, for example, volatile memory, such as static random accessmemory (SRAM) and dynamic random access memory (DRAM), and/ornon-volatile memory, such as read only memory (ROM), erasableprogrammable ROM, flash memories, hard disks, optical disks, andmagnetic tapes. In an embodiment, one or more components (not shown) ofthe system 100 can be stored in the memory 101. The memory 101 isconfigured to store operational instructions which when executed causeone or more of the hardware processor(s) 102 to perform various actionsassociated with the recommender model selection being handled by thesystem 100. The memory 101 further stores a recommendation learnedmodel, which is used by the system 100 for generating therecommendations. The recommendation learned model is trained by usinginformation pertaining to capability of a plurality of recommendationmodels and user requirements (user requirement) as training data. Forexample, the training data specifies user requirements, error measure(s)in each of the user requirements, corresponding EV and ET,specifications and capabilities of the plurality of recommender models,recommendations generated corresponding to each of the user requirementsand so on. The recommendation learned model can be pre-trained usingdata of the aforementioned type. In another embodiment, therecommendation learned model can be updated using real-time data. Thesystem 100 may use any suitable machine learning algorithm forgenerating the recommendation learned model using the training data. Therecommendation learned model is configured to collect and process theinput data from the user, and generate recommendation pertaining to oneor more recommender models as matching a user requirements. The varioussteps involved in the process of recommender model selection areexplained with description of FIG. 2 and FIG. 3. All the steps in FIG. 2and FIG. 3 are explained with reference to the system of FIG. 1.

FIG. 2 is a flow diagram depicting steps involved in the process ofgenerating a recommender model recommendation, using the system of FIG.1, according to some embodiments of the present disclosure. The system100 collects (202) a user requirements for at least one recommendermodel as input. The user requirements specify/include at least one errormeasure, and corresponding values of Error Value (EV) and ErrorTolerance (ET). The term ‘error measure’ refers to parameters of therecommender model, values of which can indicate capability/performanceof the recommender model. For example, the error measures are precision,recall, Root Mean Square Error (RMSE) and so on, and correspondingvalues. However due to various factors such as but not limited totraining data used, and type(s) of ML algorithm used for generating therecommender model, there may be deviations in values of the errormeasures. The Error Value (EV) of any error measure represents adistinct value of the error measure, and ET represents extent ofdeviation from the distinct value, for each of the recommender models.Similarly in the user input, the error measures specified indicate typeof parameters of the recommender model the user is interested in. Valueof EV for an error measure in the user input represents value of theerror measure the user expects, and value of ET represents extent ofdeviation the user is fine with (i.e. a user accepted deviation) interms of specific values of the ET. For example, the user may specifyvalue of an error measure ‘efficiency’ as 70% (i.e. EV), with an errortolerance value of ±5%. This means that the user is looking for arecommender model with at least 70% efficiency, and the ET of ±5% allowsselection of recommender models having efficiency between 65% and 75%.In various embodiments, the recommender models are stored in the memory101.

The collected user inputs are then fed as input to the recommendationlearned model, which processes the user inputs to determine the userrequirements in terms of the error measures, EV, and ET. Therecommendation learned model further determines (204) values of EV andET for at least the error measures specified in the user input, for eachof the plurality of recommender models.

The recommendation learned model further determines (206) correlationbetween the determined values of EV and ET of at least the errormeasures specified in the user input with the corresponding values of EVand ET of the error measures of the recommender models. Here, thecorrelation is established by comparing the values of EV and ET of theerror measures of the user input with that of the recommender models tofind match. All the recommender models for which the EV and ET of atleast the error measures specified in the user requirements matches thevalues of corresponding EV and ET are shortlisted. In this process, therecommendation learned model initially compares values of EV of an errormeasure of the recommender model with corresponding EV value specifiedin the user requirements. If the EV values are matching, then therecommender model is shortlisted. If the EV values are not matching,then the ET values are compared. If the extent of deviation is withinthe value of ET specified in the user requirements, then the recommendermodel is shortlisted. If the extent of deviation is not within the valueof ET specified in the user requirements, then the recommender model isdiscarded. As the ET of each of the plurality of recommender models maybe different, value of correlation value corresponding to each of therecommender models is determined. This step of determining thecorrelation value is explained in description of FIG. 3.

Based on the determined correlation, the system 100 determines (208) atleast one of the recommender model as the recommender model matching theuser requirements. The step 208 is explained further with description ofFIG. 3. Further, the system 100 generates (210) a recommendation basedon the at least one recommender model determined as matching the userrequirements. In various embodiments, one or more steps in method 200may be omitted. In another embodiment, steps in method 200 can beperformed in the same order as depicted in FIG. 2 or in any alternateorder technically feasible.

FIG. 3 is a flow diagram depicting steps involved in the process ofselecting a recommender model from a plurality of recommender models,using the system of FIG. 1, in accordance with some embodiments of thepresent disclosure. While establishing the correlation, the values of EVand ET of one or more error measures of each of the recommender modelsare compared (302) with EV and ET of the same error measures of the userinput, and determines (304) a correlation value of each of therecommender models, using equation 1. In an embodiment, the EV value mayhave preference over ET value. For example, if EV value of only one ofthe recommender models is matching the EV value in the userrequirements, then the recommender model is determined as therecommender model matching the user requirements. If EV value of none ofthe recommender models is matching the EV value in the userrequirements, only then the ET values and the corresponding correlationvalues are determined by the system 100. The correlation value of arecommender model represents extent of match between the recommendermodel and the user requirements.error_(f)=Σ_(i=1) ^(i=n)√{square root over((actual_(i)−predicted_(i))²)}  (1)

-   -   Where    -   error_(f)→Correlation value    -   actual_(i)→represents value of ET specified in the user        requirements    -   predicted_(i)→represents determined value of ET of the        recommender model

After determining the correlation values, the system 100 compares (306)correlation value of each of the plurality of recommender models withone another, and selects (308) a recommender model with highest value ofcorrelation value among the plurality of recommender models. In anembodiment, more than one recommender model are selected if the userinput specifies so, and the more than one recommender models may bearranged based on the extent of deviation (and in turn extent of matchwith the user requirements). In various embodiments, one or more stepsin method 300 may be omitted. In another embodiment, steps in method 300can be performed in the same order as depicted in FIG. 3 or in anyalternate order technically feasible.

The illustrated steps are set out to explain the exemplary embodimentsshown, and it should be anticipated that ongoing technologicaldevelopment will change the manner in which particular functions areperformed. These examples are presented herein for purposes ofillustration, and not limitation. Further, the boundaries of thefunctional building blocks have been arbitrarily defined herein for theconvenience of the description. Alternative boundaries can be defined solong as the specified functions and relationships thereof areappropriately performed. Alternatives (including equivalents,extensions, variations, deviations, etc., of those described herein)will be apparent to persons skilled in the relevant art(s) based on theteachings contained herein. Such alternatives fall within the scope ofthe disclosed embodiments. Also, the words “comprising,” “having,”“containing,” and “including,” and other similar forms are intended tobe equivalent in meaning and be open ended in that an item or itemsfollowing any one of these words is not meant to be an exhaustivelisting of such item or items, or meant to be limited to only the listeditem or items. It must also be noted that as used herein and in theappended claims, the singular forms “a,” “an,” and “the” include pluralreferences unless the context clearly dictates otherwise.

Furthermore, one or more computer-readable storage media may be utilizedin implementing embodiments consistent with the present disclosure. Acomputer-readable storage medium refers to any type of physical memoryon which information or data readable by a processor may be stored.Thus, a computer-readable storage medium may store instructions forexecution by one or more processors, including instructions for causingthe processor(s) to perform steps or stages consistent with theembodiments described herein. The term “computer-readable medium” shouldbe understood to include tangible items and exclude carrier waves andtransient signals, i.e., be non-transitory. Examples include randomaccess memory (RAM), read-only memory (ROM), volatile memory,nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, andany other known physical storage media.

It is intended that the disclosure and examples be considered asexemplary only, with a true scope of disclosed embodiments beingindicated by the following claims.

What is claimed is:
 1. A processor implemented method for recommendermodel selection, comprising: collecting user requirements as input, viaone or more hardware processors, wherein the user requirements compriseat least one error measure and corresponding at least one error value(EV) and error tolerance (ET); processing the collected userrequirements using a recommendation learned model pre-trained oninformation pertaining to capability of a plurality of recommendermodels and user requirements, via the one or more hardware processors,wherein processing the collected user requirements comprising:determining dynamically, value of EV and ET of the at least one errormeasure for each of the plurality of recommender models; determiningcorrelation between the determined value of EV and ET of the at leastone error measure for each of the plurality of recommender models withthe EV and ET in the user requirement, via the one or more hardwareprocessors; determining at least one recommender model among theplurality of recommender models as a recommender model matching the userrequirements, based on the determined correlation, via the one or morehardware processors; training a recommendation learned model by usinginformation pertaining to capability of the plurality of recommendermodels and the user requirements as training data, wherein therecommendation learned model is updated using real time data and whereinthe training data specifies the user requirements, corresponding EV andET, specifications and capabilities of the plurality of recommendermodels via the one or more hardware processors; and generating arecommendation based on the at least one recommender model determined asmatching the user requirements, via the one or more hardware processors.2. The method as claimed in claim 1, wherein the EV and ET of the atleast one error measure of the plurality of recommender models indicatescapability of the recommender model.
 3. The method as claimed in claim1, wherein determining the at least one recommender model as therecommender model matching the user requirements based on the determinedcorrelation comprises: comparing the EV and ET in the user requirementswith EV and ET of each of the plurality of the recommender models;determining a correlation value of each of the plurality of recommendermodels, wherein the correlation value of a recommender model representsextent of match between the EV and ET of the recommender model with theEV and ET in the user requirement; and selecting at least onerecommender model having highest correlation value among the pluralityof recommender models as the at least one recommender model matching theuser requirements.
 4. A system for recommender model selection,comprising: one or more hardware processors; one or more communicationinterfaces; and one or more memory storing a plurality of instructions,wherein the plurality of instructions when executed cause the one ormore hardware processors to: collect user requirements as input, via oneor more hardware processors, wherein the user requirements comprise atleast one error measure and corresponding at least one error value (EV)and error tolerance (ET); process the collected user requirements usinga recommendation learned model pre-trained on information pertaining tocapability of a plurality of recommender models and user requirements,via the one or more hardware processors, wherein processing thecollected user requirements comprising: determining dynamically, valueof EV and ET of the at least one error measure for each of the pluralityof recommender models; determining correlation between the determinedvalue of EV and ET of the at least one error measure for each of theplurality of recommender models with the EV and ET in the userrequirements; determining at least one recommender model among theplurality of recommender models as a recommender model matching the userrequirements, based on the determined correlation; train arecommendation learned model by using information pertaining tocapability of the plurality of recommender models and the userrequirements as training data, wherein the recommendation learned modelis updated using real time data and wherein the training data specifiesthe user requirements, corresponding EV and ET, specifications andcapabilities of the plurality of recommender models; and generate arecommendation based on the at least one recommender model determined asmatching the user requirements.
 5. The system as claimed in claim 4,wherein the EV and ET of the at least one error measure of the pluralityof recommender models indicates capability of the recommendation model.6. The system as claimed in claim 4, wherein the system determines theat least one recommender model as the recommender model matching theuser requirements based on the determined correlation by: comparing theEV and ET in the user requirement with EV and ET of each of theplurality of the recommender models; determining a correlation value ofeach of the plurality of recommender models, wherein the correlationvalue of a recommender model represents extent of match between the EVand ET of the recommender model with the EV and ET in the userrequirement; and selecting at least one recommender model having highestcorrelation value among the plurality of recommender models as the atleast one recommender model matching the user requirements.
 7. Anon-transitory computer readable medium for recommender model selection,the non-transitory computer readable medium performs the recommendermodel selection by: collecting user requirements as input, via one ormore hardware processors, wherein the user requirements comprise atleast one error measure and corresponding at least one error value (EV)and error tolerance (ET); processing the collected user requirementsusing a recommendation learned model pre-trained on informationpertaining to capability of a plurality of recommender models and userrequirements, via the one or more hardware processors, whereinprocessing the collected user requirements comprising: determiningdynamically, value of EV and ET of the at least one error measure foreach of the plurality of recommender models; determining correlationbetween the determined value of EV and ET of the at least one errormeasure for each of the plurality of recommender models with the EV andET in the user requirement, via the one or more hardware processors; anddetermining at least one recommender model among the plurality ofrecommender models as a recommender model matching the userrequirements, based on the determined correlation, via the one or morehardware processors; training a recommendation learned model by usinginformation pertaining to capability of the plurality of recommendermodels and the user requirements as training data, wherein therecommendation learned model is updated using real time data and whereinthe training data specifies the user requirements, corresponding EV andET, specifications and capabilities of the plurality of recommendermodels via the one or more hardware processors; and generating arecommendation based on the at least one recommender model determined asmatching the user requirements, via the one or more hardware processors.8. The non-transitory computer readable medium as claimed in claim 7,wherein the EV and ET of the at least one error measure of the pluralityof recommender models indicates capability of the recommender model. 9.The non-transitory computer readable medium as claimed in claim 7,wherein determining the at least one recommender model as therecommender model matching the user requirements based on the determinedcorrelation comprises: comparing the EV and ET in the user requirementswith EV and ET of each of the plurality of the recommender models;determining a correlation value of each of the plurality of recommendermodels, wherein the correlation value of a recommender model representsextent of match between the EV and ET of the recommender model with theEV and ET in the user requirement; and selecting at least onerecommender model having highest correlation value among the pluralityof recommender models as the at least one recommender model matching theuser requirements.